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Avoiding parameter growth of TSK fuzzy models
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| Author(s) |
Jacek Kabziñski |
| Abstract |
We propose two relatively simple and effective procedures for creating neuro-fuzzy Takagi-Sugeno-Kang model and for tuning of TSK model parameters together with the rule-base structure optimisation. The main advantage of the first method is that the initial structure and parameters are set properly, so we need a few training iterations for the neural network representation of our model to converge. In the second approach the most important is rule reduction procedure –annihilation and fusion incorporated in a genetic optimisation algorithm. Numerical examples are provided. |
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| Filename: | 153 |
| Filesize: | 415.5 KB |
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| Type |
Members Only |
| Date |
Last modified 2006-02-07 by System |
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